Title: Robust C-Loss Kernel Classifiers
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| Authors: Xu, GB; Hu, BG; Principe, JC
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| Author Full Names: Xu, Guibiao; Hu, Bao-Gang; Principe, Jose C.
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| Source: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 29 (3):510-522; 10.1109/TNNLS.2016.2637351 MAR 2018
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| Language: English
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| Abstract: The correntropy-induced loss (C-loss) function has the nice property of being robust to outliers. In this paper, we study the C-loss kernel classifier with the Tikhonov regularization term, which is used to avoid overfitting. After using the half-quadratic optimization algorithm, which converges much faster than the gradient optimization algorithm, we find out that the resulting C-loss kernel classifier is equivalent to an iterative weighted least square support vector machine (LS-SVM). This relationship helps explain the robustness of iterative weighted LS-SVM from the correntropy and density estimation perspectives. On the large-scale data sets which have low-rank Gram matrices, we suggest to use incomplete Cholesky decomposition to speed up the training process. Moreover, we use the representer theorem to improve the sparseness of the resulting C-loss kernel classifier. Experimental results confirm that our methods are more robust to outliers than the existing common classifiers.
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| ISSN: 2162-237X
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| eISSN: 2162-2388
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| IDS Number: FX8LD
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| Unique ID: WOS:000426344600001
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| PubMed ID: 28055924
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